vi_shap: SHAP-based variable importance

In vip: Variable Importance Plots

SHAP-based variable importance

Description

Compute SHAP-based VI scores for the predictors in a model. See details below.

Usage

vi_shap(object, ...)

## Default S3 method:
vi_shap(object, feature_names = NULL, train = NULL, ...)

Arguments

object	A fitted model object (e.g., a randomForest object).
...	Additional arguments to be passed on to fastshap::explain() (e.g., nsim = 30, adjust = TRUE, or avprediction wrapper via the pred_wrapper argument); see ?fastshap::explain for details on these and other useful arguments.
feature_names	Character string giving the names of the predictor variables (i.e., features) of interest. If NULL (the default) then they will be inferred from the train and target arguments (see below). It is good practice to always specify this argument.
train	A matrix-like R object (e.g., a data frame or matrix) containing the training data. If NULL (the default) then the internal get_training_data() function will be called to try and extract it automatically. It is good practice to always specify this argument.

Details

This approach to computing VI scores is based on the mean absolute value of the SHAP values for each feature; see, for example, https://github.com/shap/shap and the references therein.

Strumbelj, E., and Kononenko, I. Explaining prediction models and individual predictions with feature contributions. Knowledge and information systems 41.3 (2014): 647-665.

Value

A tidy data frame (i.e., a tibble object) with two columns:

• Variable - the corresponding feature name;

• Importance - the associated importance, computed as the mean absolute Shapley value.

Examples

## Not run: 
library(ggplot2)  # for theme_light() function
library(xgboost)

# Simulate training data
trn <- gen_friedman(500, sigma = 1, seed = 101)  # ?vip::gen_friedman

# Feature matrix
X <- data.matrix(subset(trn, select = -y))  # matrix of feature values

# Fit an XGBoost model; hyperparameters were tuned using 5-fold CV
set.seed(859)  # for reproducibility
bst <- xgboost(X, label = trn$y, nrounds = 338, max_depth = 3, eta = 0.1,
               verbose = 0)

# Construct VIP using "exact" SHAP values from XGBoost's internal Tree SHAP
# functionality
vip(bst, method = "shap", train = X, exact = TRUE, include_type = TRUE,
    geom = "point", horizontal = FALSE,
    aesthetics = list(color = "forestgreen", shape = 17, size = 5)) +
  theme_light()

# Use Monte-Carlo approach, which works for any model; requires prediction
# wrapper
pfun_prob <- function(object, newdata) {  # prediction wrapper
  # For Shapley explanations, this should ALWAYS return a numeric vector
  predict(object, newdata = newdata, type = "prob")[, "yes"]
}

# Compute Shapley-based VI scores
set.seed(853)  # for reproducibility
vi_shap(rfo, train = subset(t1, select = -survived), pred_wrapper = pfun_prob,
        nsim = 30)
## # A tibble: 5 × 2
## Variable Importance
##   <chr>         <dbl>
## 1 pclass       0.104
## 2 age          0.0649
## 3 sex          0.272
## 4 sibsp        0.0260
## 5 parch        0.0291

## End(Not run)

vip documentation built on Aug. 21, 2023, 5:12 p.m.